Experiment 1a

Contingencies

Low Value Options

Fixed: 20 (Door 3)
Risky: 0 or 40 (Door 4)

High Value Options

Fixed: 60 (Door 1)
Risky: 40 or 80 (Door 2)

Trials

Six blocks of 80 trials.
Decision Trials: (fixed low vs risky low) & (fixed high vs risky high) - 24 per block (12 low, 12 high)
Single door trials: one door to click - 40 per block
Catch trials: high vs low - 16 per block

EX 80-20

Block 1: 80% extreme values (0 low, 80 high)
Block 2: 50% extreme values.
Block 3: 20% extreme values (0 low, 80 high)
Block 4-6: Block 2: 50% extreme values.

EX 20-80

Block 1: 20% extreme values (0 low, 80 high)
Block 2: 50% extreme values.
Block 3: 80% extreme values (0 low, 80 high)
Block 4-6: 50% extreme values.

EX 50-50 (control)

All blocks: 50% extreme values.

Participant Information

Pre-catch exclusion

N = 192

group	gender	n	age_mean	age_sd	age_median	age_IQR
EX 50-50	F	38	19.132	1.711	18	1.75
EX 50-50	M	25	20.320	3.198	19	3.00
EX 80-20	F	38	19.447	3.644	18	1.75
EX 80-20	M	28	19.607	2.347	19	2.00
EX 20-80	F	39	20.179	4.667	19	2.00
EX 20-80	M	24	19.375	1.952	19	2.00

Post-catch exclusion

N = 184

group	gender	n	age_mean	age_sd	age_median	age_IQR
EX 50-50	F	36	19.194	1.737	18.5	2
EX 50-50	M	22	20.318	3.272	19.0	3
EX 80-20	F	37	19.378	3.669	18.0	1
EX 80-20	M	27	19.519	2.343	19.0	2
EX 20-80	F	39	20.179	4.667	19.0	2
EX 20-80	M	23	19.435	1.973	19.0	2

Condition totals:

group	n	age_mean	age_sd	age_median	age_IQR
EX 50-50	58	19.621	2.470	19	2
EX 80-20	64	19.438	3.157	19	2
EX 20-80	62	19.903	3.887	19	2

Total number of catch trial exclusions = 8.
Catch trial exclusions were based on block 5 & 6 performance.

Catch Trials

Plot - Block means with 95% CIs

Risk Preference Trials

Plot - Block means with 95% CIs

Plot - Interaction plot with 95% CIs (Block 5 & 6)

Main Effects

ANOVA Table

	Model	df	AIC	BIC	logLik	Test	L.Ratio	p.value	BF_10
base_mod	1	4	226.496	242.128	-109.248		NA	NA	NA
grp_mod	2	6	228.323	251.772	-108.162	1 vs 2	2.172	0.338	8.000000e-03
value_mod	3	7	176.484	203.840	-81.242	2 vs 3	53.840	0.000	2.559852e+10
grp_val	4	9	179.296	214.469	-80.648	3 vs 4	1.188	0.552	5.000000e-03

Main Effect of Group:

\(\chi^2(2) = 2.17, p = 0.338, BF_{10} < .01\)

Main Effect of Choice Value (i.e., Extreme Outcome effect):

\(\chi^2(1) = 53.84, p < .001, BF_{10} > 150\)

Interaction: Group \(\times\) Choice Value:

\(\chi^2(2) = 1.19, p = 0.552, BF_{10} < .01\)

Planned Comparisons

ANOVA Table

	Value	Std.Error	DF	t-value	p-value	sig	r_effect
(Intercept)	0.413	0.034	181	12.219	0.000	TRUE	0.672
group 8020_vs_50	0.041	0.047	181	0.872	0.384	FALSE	0.065
group 2080_vs_50	-0.026	0.047	181	-0.552	0.581	FALSE	0.041
choice_value1	0.102	0.024	181	4.336	0.000	TRUE	0.307
group 8020_vs_50 :choice_value1	-0.014	0.032	181	-0.435	0.664	FALSE	0.032
group 2080_vs_50 :choice_value1	0.020	0.033	181	0.620	0.536	FALSE	0.046

EX 80-20 vs. EX 50-50:

\(b = 0.041, t(181) = 0.872, p = 0.384, r = 0.065\)

EX 20-80 vs. EX 50-50:

\(b = -0.026, t(181) = -0.552, p = 0.581, r = 0.041\)

EX 80-20 vs. EX 50-50 | Choice Value (High vs. Low):

\(b = -0.014, t(181) = -0.435, p = 0.664, r = 0.032\)

EX 20-80 vs. EX 50-50 | Choice Value (High vs. Low):

\(b = 0.02, t(181) = 0.62, p = 0.536, r = 0.046\)

Notes

All plots and analyses are showing the combined effect of block 5 and 6.
Bayes Factors are reported as Inverse Bayes Factors, this means larger values indicate greater support for the effect. Anything less than 1 should be considered as contributing no evidence.

First-Outcome Judgement

Plot - Proportions

Note:

Proportions are taken within group and context (High/Low). E.g., All three bars in the top left facet sum to 1, all three bars in the top middle facet sum to 1, and so on.
Since non-numeric responses were permissible by the experiment’s program, data was filtered to remove all non-numeric inputs. If a participant’s inputs had a cumulative sum less than 20 or greater than 300, their results were removed. In total, 8 participants met this criteria.

2x3 Chi-Squared Tests

High Value Results

High Value Contingency Table
	No	Yes
EX 50-50	11	38
EX 80-20	13	44
EX 20-80	16	39

\(\chi^2(2) = 0.81, p = 0.667, \varphi_c = 0.07\)

Cannot rule out that recalling the extreme value is independent of group.

Standardized Residuals
	No	Yes
EX 50-50	-0.465	0.465
EX 80-20	-0.443	0.443
EX 20-80	0.898	-0.898

p-values
	No	Yes
EX 50-50	0.642	0.642
EX 80-20	0.658	0.658
EX 20-80	0.369	0.369

None of the three conditions differ significantly from the expected value.

Low Value Results

Low Value Contingency Table
	No	Yes
EX 50-50	12	37
EX 80-20	8	49
EX 20-80	7	48

\(\chi^2(2) = 3.04, p = 0.219, \varphi_c = 0.14\)

Cannot rule out that recalling the extreme value is independent of group.

Standardized Residuals
	No	Yes
EX 50-50	1.734	-1.734
EX 80-20	-0.688	0.688
EX 20-80	-0.989	0.989

p-values
	No	Yes
EX 50-50	0.083	0.083
EX 80-20	0.492	0.492
EX 20-80	0.323	0.323

None of the three conditions differ significantly from the expected value.

Post-Hoc Pairwise Fisher-Exact Tests

value	comparison	odds_ratio	p_value	CI_low	CI_upper	p_value_adj
High	EX 50-50 : EX 80-20	0.9799	1.0000	0.3521	2.6883	1.0000
High	EX 50-50 : EX 20-80	0.7080	0.5056	0.2603	1.8687	1.0000
High	EX 80-20 : EX 20-80	0.7223	0.5201	0.2805	1.8310	1.0000
Low	EX 50-50 : EX 80-20	1.9735	0.2157	0.6633	6.1822	1.0000
Low	EX 50-50 : EX 20-80	2.2068	0.1357	0.7174	7.3155	0.8141
Low	EX 80-20 : EX 20-80	1.1184	1.0000	0.3256	3.9337	1.0000

Nothing reached significance.

Note:

In the contingency tables, ‘Yes’ indicates the extreme value was recalled, ‘No’ indicates that it was not.
Only recalled outcomes that matched the programmed values were included. i.e., if they saw the High Value Risky Door, 80 was logged as “Yes” and 40 was logged as “No.” If they saw the Low Value Risky Door, 0 was logged as “Yes”, 40 was logged as “No”. Participants who responded with any “other” values were removed from the analysis. Only Risky doors were evaluated.
In total, removing “other” responses resulted 15 participants being excluded from these analyses leaving a final N = 161.
\(\varphi_c\) is Cramér’s V, a measure of association for two nominal variables (i.e., an effect size of sorts). It can take a value between 0 and 1, with 0 indicating no association (i.e., full independence).
Post-hoc analysis on each \(\chi^2\) test was conducted by evaluating the standardized residuals from the chi-square analysis because it seemed the most intuitive approach. However, pairwise Pearson Chi-squared or Fisher Exact tests across the three conditions were also run. The p_value_adj column shows corrected p-values using the Holm-Bonferroni method.

Frequency Judgement

Plot - Means and 95% CIs

2x3 ANOVA using linear mixed-effects models fit by maximum likelihood

Main Effects

	Model	df	AIC	BIC	logLik	Test	L.Ratio	p.value	BF_10
base	1	4	3240.858	3256.490	-1616.429		NA	NA	NA
cond_mod	2	6	3244.737	3268.185	-1616.368	1 vs 2	0.121	0.941	0.003
HL_mod	3	7	3221.010	3248.366	-1603.505	2 vs 3	25.727	0.000	20117.182
int_mod	4	9	3224.021	3259.194	-1603.010	3 vs 4	0.989	0.610	0.004

Main Effect of Group:

\(\chi^2(2) = 0.121\), \(p = 0.941\), \(BF_{10} < .01\)

Main Effect of Value (High vs. Low):

\(\chi^2(1) = 25.727\), \(p < 0.001\), \(BF_{10} > 150\)

Interaction:

\(\chi^2(2) = 0.989\), \(p = 0.61\), \(BF_{10} < .01\)

Planned Contrasts

	Value	Std.Error	DF	t-value	p-value	r_effect
(Intercept)	55.991	1.819	181	30.778	0.000	0.916
group 8020_vs_50	0.454	2.512	181	0.181	0.857	0.013
group 2080_vs_50	-0.403	2.531	181	-0.159	0.874	0.012
fj_value1	-6.026	1.714	181	-3.517	0.001	0.253
group 8020_vs_50 :fj_value1	2.252	2.366	181	0.952	0.342	0.071
group 2080_vs_50 :fj_value1	0.647	2.384	181	0.271	0.786	0.020

EX 80-20 vs. EX 50-50:

\(b = 0.454\), \(t(181) = 0.181\), \(p = 0.857\), \(r = 0.013\)

EX 20-80 vs. EX 50-50:

\(b = -0.403\), \(t(181) = -0.159\), \(p = 0.874\), \(r = 0.012\)

EX 80-20 vs. EX 50-50 | Value (High vs. Low):

\(b = 2.252\), \(t(181) = 0.952\), \(p = 0.342\), \(r = 0.071\)

EX 20-80 vs. EX 50-50 | Value (High vs. Low):

\(b = 0.647\), \(t(181) = 0.271\), \(p = 0.786\), \(r = 0.02\)

Notes

ANOVA was conducted only on judgements for the risky extreme values (0 and +80).
Bayes Factors are reported as Inverse Bayes Factors, this means larger values indicate greater support for the effect. Anything less than 1 should be considered as contributing no evidence.

Experiment 1b

Contingencies

Low Value Options

Fixed: 20 (Door 3)
Risky: 0 or 40 (Door 4)

High Value Options

Fixed: 60 (Door 1)
Risky: 40 or 80 (Door 2)

Trials

Six blocks of 80 trials.
Decision Trials: (fixed low vs risky low) & (fixed high vs risky high) - 24 per block (12 low, 12 high)
Single door trials: one door to click - 40 per block
Catch trials: high vs low - 16 per block

BEST 80-20

Block 1: 80% chance of best outcome on risky options.
Block 2: 50% of best outcome on risky options.
Block 3: 20% chance of best outcome on risky options.
Block 4-6: 50% of best outcome on risky options.

BEST 20-80

Block 1: 20% chance of best outcome on risky options.
Block 2: 50% of best outcome on risky options.
Block 3: 80% chance of best outcome on risky options.
Block 4-6: 50% of best outcome on risky options.

BEST 50-50 (control)

All blocks: 50% of best outcome on risky options.

Participant Information

Pre-catch exclusion

N = 190

group	gender	n	age_mean	age_sd	age_median	age_IQR
BEST 50-50	F	50	19.220	3.699	18.5	1
BEST 50-50	M	16	19.375	1.544	19.0	2
BEST 80-20	F	48	19.500	3.156	18.0	1
BEST 80-20	M	13	19.231	1.787	19.0	1
BEST 20-80	F	38	18.842	1.636	18.0	1
BEST 20-80	M	25	19.840	1.748	19.0	2

Post-catch exclusion

N = 183

group	gender	n	age_mean	age_sd	age_median	age_IQR
BEST 50-50	F	46	19.326	3.842	19	1
BEST 50-50	M	15	19.467	1.552	19	2
BEST 80-20	F	48	19.500	3.156	18	1
BEST 80-20	M	13	19.231	1.787	19	1
BEST 20-80	F	36	18.889	1.670	18	1
BEST 20-80	M	25	19.840	1.748	19	2

Condition totals:

group	n	age_mean	age_sd	age_median	age_IQR
BEST 50-50	61	19.361	3.411	19	1
BEST 80-20	61	19.443	2.907	18	1
BEST 20-80	61	19.279	1.752	19	2

Total number of catch trial exclusions = 7.
Catch trial exclusions were based on block 5 and 6 performance.

Catch Trials

Plot - Block means with 95% CIs

Risk Preference Trials

Plot - Block means with 95% CIs

Plot - Interaction plot with 95% CIs (Block 5 & 6)

Main Effects

ANOVA Table

	Model	df	AIC	BIC	logLik	Test	L.Ratio	p.value	BF_10
base_mod	1	4	247.353	262.964	-119.677		NA	NA	NA
grp_mod	2	6	249.708	273.124	-118.854	1 vs 2	1.645	0.439	6.000000e-03
value_mod	3	7	157.116	184.435	-71.558	2 vs 3	94.592	0.000	1.813658e+19
grp_val	4	9	153.345	188.469	-67.673	3 vs 4	7.771	0.021	1.330000e-01

Main Effect of Group:

\(\chi^2(2) = 1.65, p = 0.439, BF_{10} < .01\)

Main Effect of Choice Value (i.e., Extreme Outcome effect):

\(\chi^2(1) = 94.59, p < .001, BF_{10} > 150\)

Interaction: Group \(\times\) Choice Value:

\(\chi^2(2) = 7.77, p = 0.021, BF_{10} = 0.133\)

Note: while the p-value is technically significant, the Bayes Factor implies the interaction contributes nothing worthwhile.

Planned Comparisons

ANOVA Table

	Value	Std.Error	DF	t-value	p-value	sig	r_effect
(Intercept)	0.383	0.033	180	11.747	0.000	TRUE	0.659
group 8020_vs_50	0.024	0.046	180	0.511	0.610	FALSE	0.038
group 2080_vs_50	0.058	0.046	180	1.267	0.207	FALSE	0.094
choice_value1	0.159	0.022	180	7.329	0.000	TRUE	0.479
group 8020_vs_50 :choice_value1	0.013	0.031	180	0.435	0.664	FALSE	0.032
group 2080_vs_50 :choice_value1	-0.067	0.031	180	-2.173	0.031	TRUE	0.160

BEST 80-20 vs. BEST 50-50:

\(b = 0.024, t(180) = 0.511, p = 0.61, r = 0.038\)

BEST 20-80 vs. BEST 50-50:

\(b = 0.058, t(180) = 1.267, p = 0.207, r = 0.094\)

BEST 80-20 vs. BEST 50-50 | Choice Value (High vs. Low):

\(b = 0.013, t(180) = 0.435, p = 0.664, r = 0.032\)

BEST 20-80 vs. BEST 50-50 | Choice Value (High vs. Low):

\(b = -0.067, t(180) = -2.173, p = 0.031, r = 0.16\)

Notes

All plots and analyses are showing the combined effect of block 5 and 6.
Bayes Factors are reported as Inverse Bayes Factors, this means larger values indicate greater support for the effect. Anything less than 1 should be considered as contributing no evidence.

First-Outcome Judgement

Plot - Proportions

Note:

Proportions are taken within group and context (High/Low). E.g., All three bars in the top left facet sum to 1, all three bars in the top middle facet sum to 1, and so on.
Since non-numeric responses were permissible by the experiment’s program, data was filtered to remove all non-numeric inputs. If a participant’s inputs had a cumulative sum less than 20 or greater than 300, their results were removed. In total, 6 participants met this criteria.

2x3 Chi-Squared Tests

High Value Results

High Value Contingency Table
	No	Yes
BEST 50-50	15	43
BEST 80-20	10	47
BEST 20-80	23	30

\(\chi^2(2) = 9.31, p = 0.01, \varphi_c = 0.24\)

Recalling the extreme value is dependent on condition.

Standardized Residuals
	No	Yes
BEST 50-50	-0.56	0.56
BEST 80-20	-2.27	2.27
BEST 20-80	2.89	-2.89

p-values
	No	Yes
BEST 50-50	0.572	0.572
BEST 80-20	0.023	0.023
BEST 20-80	0.004	0.004

The BEST 80-20 and BEST 20-80 conditions both differ significantly from the expected value. The BEST 80-20 condition tends to recall the extreme value, whereas BEST 20-80 does not.

BEST 80-20: \(z = 2.27, p = 0.023\)
BEST 20-80: \(z = -2.89, p = 0.004\)

Low Value Results

Low Value Contingency Table
	No	Yes
BEST 50-50	5	53
BEST 80-20	12	45
BEST 20-80	7	46

\(\chi^2(2) = 3.7, p = 0.157, \varphi_c = 0.15\)

Cannot rule out that recalling the extreme value is independent of group.

Standardized Residuals
	No	Yes
BEST 50-50	-1.524	1.524
BEST 80-20	1.796	-1.796
BEST 20-80	-0.271	0.271

p-values
	No	Yes
BEST 50-50	0.128	0.128
BEST 80-20	0.072	0.072
BEST 20-80	0.786	0.786

None of the three conditions differ significantly from the expected value.

Post-Hoc Pairwise Fisher-Exact Tests

value	comparison	odds_ratio	p_value	CI_low	CI_upper	p_value_adj
High	BEST 50-50 : BEST 80-20	1.6325	0.3667	0.6105	4.5340	0.9612
High	BEST 50-50 : BEST 20-80	0.4583	0.0713	0.1883	1.0884	0.3526
High	BEST 80-20 : BEST 20-80	0.2809	0.0037	0.1037	0.7154	0.0223
Low	BEST 50-50 : BEST 80-20	0.3569	0.0705	0.0914	1.1908	0.3526
Low	BEST 50-50 : BEST 20-80	0.6226	0.5456	0.1453	2.4571	0.9612
Low	BEST 80-20 : BEST 20-80	1.7435	0.3204	0.5713	5.7317	0.9612

Recalling the extreme value was found to be dependent on condition (\(p = 0.022\)) only when high value options in the BEST 80-20 and BEST 20-80 conditions are considered (Row 3 in the table). Everything else fails to reach significance.

Note:

In the contingency tables, ‘Yes’ indicates the extreme value was recalled, ‘No’ indicates that it was not.
Only recalled outcomes that matched the programmed values were included. i.e., if they saw the High Value Risky Door, 80 was logged as “Yes” and 40 was logged as “No.” If they saw the Low Value Risky Door, 0 was logged as “Yes”, 40 was logged as “No”. Participants who responded with any “other” values were removed from the analysis. Only Risky doors were evaluated.
In total, removing “other” responses resulted 9 participants being excluded from these analyses leaving a final N = 168.
\(\varphi_c\) is Cramér’s V, a measure of association for two nominal variables (i.e., an effect size of sorts). It can take a value between 0 and 1, with 0 indicating no association (i.e., full independence).
Post-hoc analysis on each \(\chi^2\) test was conducted by evaluating the standardized residuals from the chi-square analysis because it seemed the most intuitive approach. However, pairwise Pearson Chi-squared or Fisher Exact tests across the three conditions were also run. The p_value_adj column shows corrected p-values using the Holm-Bonferroni method.

Frequency Judgement

Plot - Means and 95% CIs

2x3 ANOVA using linear mixed-effects models fit by maximum likelihood

Main Effects

	Model	df	AIC	BIC	logLik	Test	L.Ratio	p.value	BF_10
base	1	4	3215.285	3230.896	-1603.643		NA	NA	NA
cond_mod	2	6	3211.195	3234.611	-1599.598	1 vs 2	8.090	0.018	1.560000e-01
HL_mod	3	7	3145.344	3172.662	-1565.672	2 vs 3	67.851	0.000	2.831444e+13
int_mod	4	9	3148.096	3183.220	-1565.048	3 vs 4	1.248	0.536	5.000000e-03

Main Effect of Condition:

\(\chi^2(2) = 8.09\), \(p = 0.018\), \(BF_{10} = 0.156\)

Main Effect of Value (High vs. Low):

\(\chi^2(1) = 67.851\), \(p < 0.001\), \(BF_{10} > 150\)

Interaction:

\(\chi^2(2) = 1.248\), \(p = 0.536\), \(BF_{10} < .01\)

Planned Contrasts

	Value	Std.Error	DF	t-value	p-value	r_effect
(Intercept)	59.779	1.589	180	37.609	0.000	0.942
group 8020_vs_50	-2.648	2.248	180	-1.178	0.240	0.087
group 2080_vs_50	-6.943	2.248	180	-3.089	0.002	0.224
fj_value1	-7.844	1.589	180	-4.935	0.000	0.345
group 8020_vs_50 :fj_value1	1.205	2.248	180	0.536	0.593	0.040
group 2080_vs_50 :fj_value1	-1.287	2.248	180	-0.572	0.568	0.043

BEST 80-20 vs. BEST 50-50:

\(b = -2.648\), \(t(180) = -1.178\), \(p = 0.24\), \(r = 0.087\)

BEST 20-80 vs. BEST 50-50:

\(b = -6.943\), \(t(180) = -3.089\), \(p = 0.002\), \(r = 0.224\)

BEST 80-20 vs. BEST 50-50 | Value (High vs. Low):

\(b = 1.205\), \(t(180) = 0.536\), \(p = 0.593\), \(r = 0.04\)

BEST 20-80 vs. BEST 50-50 | Value (High vs. Low):

\(b = -1.287\), \(t(180) = -0.572\), \(p = 0.568\), \(r = 0.043\)

Notes

ANOVA was conducted only on judgements for the risky extreme values (0 and +80).
Bayes Factors are reported as Inverse Bayes Factors, this means larger values indicate greater support for the effect. Anything less than 1 should be considered as contributing no evidence.

R Citations

R version 4.4.2 (2024-10-31 ucrt)

ggh4x (0.2.8) (van den Brand, 2024)
Hmisc (5.1.3) (Harrell Jr, 2024)
nlme (3.1.166) (Pinheiro et al., 2023)
RColorBrewer (1.1.3) (Neuwirth, 2022)
rcompanion (2.4.36) (Mangiafico, 2024)
tidyverse (2.0.0) (Wickham et al., 2019)

References

Harrell Jr, F. E. (2024). Hmisc: Harrell miscellaneous. https://CRAN.R-project.org/package=Hmisc

Mangiafico, S. S. (2024). rcompanion: Functions to support extension education program evaluation. Rutgers Cooperative Extension. https://CRAN.R-project.org/package=rcompanion/

Neuwirth, E. (2022). RColorBrewer: ColorBrewer palettes. https://CRAN.R-project.org/package=RColorBrewer

Pinheiro, J., Bates, D., & R Core Team. (2023). Nlme: Linear and nonlinear mixed effects models. https://CRAN.R-project.org/package=nlme

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

van den Brand, T. (2024). ggh4x: Hacks for ’ggplot2’. https://CRAN.R-project.org/package=ggh4x

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Win Big Results

2025-05-06

Experiment 1a

Low Value Options

High Value Options

Trials

EX 80-20

EX 20-80

EX 50-50 (control)

Participant Information

Condition totals:

Catch Trials

Risk Preference Trials

ANOVA Table

ANOVA Table

First-Outcome Judgement

High Value Results

Low Value Results

Post-Hoc Pairwise Fisher-Exact Tests

Frequency Judgement

Main Effects

Planned Contrasts

Experiment 1b

Low Value Options

High Value Options

Trials

BEST 80-20

BEST 20-80

BEST 50-50 (control)

Participant Information

Condition totals:

Catch Trials

Risk Preference Trials

ANOVA Table

ANOVA Table

First-Outcome Judgement

High Value Results

Low Value Results

Post-Hoc Pairwise Fisher-Exact Tests

Frequency Judgement

Main Effects

Planned Contrasts

R Citations

References